UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest

Abstract

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h–1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11% higher than at riparian sites across all seasons. The heterogeneity of ET was lower at upland sites than at riparian sites, and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world.

Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model

“…Nextly, we tried to replace weight and height with BMI, GVIF value decreased significantly (maxGVIF = 16.40). The results show that interaction is reduced significantly, but it still doesn’t reach the conditions for naive bayesian (GVIF < 3) [37, 38]. This can indicate that BMI index tend to change with age.…”

Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

“…We assessed pixel-level ET patterns across four study plots in a tropical lowland rainforest with a UAV-based thermography approach and subsequent energy balance modelling. The therein applied energy balance model DATTUTDUT (Timmermans et al, 2015) and the associated workflow from image acquisition to high-resolution ET orthomosaic maps had previously been established and validated against ground-based reference measurements such as eddy covariance and sap flux measurements across different land-use systems in the same study region (Ellsäßer, Röll, Ahongshangbam, et al, 2020;Ellsäßer, Röll, Stiegler, et al, 2020;Ellsäßer et al, 2021) and including study sites in lowland rainforest (Bulusu et al, 2023). The mean midday hourly ET rates for our lowland rainforest study plots under mostly cloud-free conditions (0.5-0.8 mm h À1 ) are similar to values from a nearby commercial, mature oil palm plantation, with mean ET of 0.4 mm h À1 and peaks up to 0.9 mm h À1 (Ellsäßer et al, 2021).…”